FIA NLS Models: Biomass Growth vs. Biomass – Additional Growth Estimators
JAH
2023-04-20
Background
This analysis document compliments FIA NLS Models: Biomass Growth vs. Biomass. All of the background information from that document applies to these analyses, which are extensions to them. The difference between that document and this analysis is the use of different growth estimators.
Here, we fit the models using: 1) calculated plot biomass growth (Mass-Balance method) using only trees >5 inches (12.5 cm) dbh (\(G_{MassBal > 5}\)), and 2) plot biomass growth (tree incremental growth method \(G_{TI}\) for trees >5 inches (12.5 cm) dbh (\(G_{TI-NoIngrow}\)).
Below the model fitting procedure is implemented by ecoprovince:
Analysis 1: \(G_{MassBal > 5}\)
211 - Northeastern Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6822 6268.0
## 2 6821 6253.9 1 14.031 15.303 9.244e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 26129.23
## 2 2 26115.94##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.0226 0.2280 4.485 7.41e-06 ***
## alpha 0.1515 0.0381 3.975 7.10e-05 ***
## A 3.5870 0.1490 24.072 < 2e-16 ***
## k 32.1364 1.7469 18.396 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9575 on 6821 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 5.408e-07
## (52 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_211, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_211, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_211, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 26115.94
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.0226 0.2280 4.485 7.41e-06 ***
## alpha 0.1515 0.0381 3.975 7.10e-05 ***
## A 3.5870 0.1490 24.072 < 2e-16 ***
## k 32.1364 1.7469 18.396 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9575 on 6821 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 5.408e-07
## (52 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: Removed 17 rows containing missing values (`geom_point()`).## Warning: Removed 1038 rows containing missing values (`geom_line()`).
plotting 2
212 - Laurentian Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 18911 19584
## 2 18910 19475 1 109.64 106.46 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 67445.37
## 2 2 67341.19##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.33873 0.18303 7.314 2.69e-13 ***
## alpha 0.27311 0.02566 10.643 < 2e-16 ***
## A 3.21539 0.09883 32.535 < 2e-16 ***
## k 43.84726 1.49165 29.395 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.015 on 18910 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 1.353e-06
## (3801 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_212, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_212, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_212, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 67341.19
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.33873 0.18303 7.314 2.69e-13 ***
## alpha 0.27311 0.02566 10.643 < 2e-16 ***
## A 3.21539 0.09883 32.535 < 2e-16 ***
## k 43.84726 1.49165 29.395 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.015 on 18910 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 1.353e-06
## (3801 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 1926 rows containing missing values (`geom_point()`).## Warning: Removed 1031 rows containing missing values (`geom_line()`).
plotting 2
221 - Eastern Broadleaf Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 7266 9818.9
## 2 7265 9612.0 1 206.84 156.33 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 32046.10
## 2 2 31893.34##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.86021 0.11923 -7.215 5.94e-13 ***
## alpha 0.52324 0.03993 13.102 < 2e-16 ***
## A 5.93232 0.20274 29.261 < 2e-16 ***
## k 32.54201 2.57514 12.637 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.15 on 7265 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 2.662e-06
## (64 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_221, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_221, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_221, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 31893.34
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.86021 0.11923 -7.215 5.94e-13 ***
## alpha 0.52324 0.03993 13.102 < 2e-16 ***
## A 5.93232 0.20274 29.261 < 2e-16 ***
## k 32.54201 2.57514 12.637 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.15 on 7265 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 2.662e-06
## (64 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 32 rows containing missing values (`geom_point()`).## Warning: Removed 1036 rows containing missing values (`geom_line()`).
plotting 2
222 - Midwest Broadleaf Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4839 5394.3
## 2 4838 5314.0 1 80.317 73.123 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 19469.00
## 2 2 19398.36##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.06034 0.21585 0.280 0.78
## alpha 0.42894 0.04778 8.978 <2e-16 ***
## A 5.05639 0.23881 21.174 <2e-16 ***
## k 41.09173 2.77384 14.814 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.048 on 4838 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 5.802e-06
## (1003 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_222, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_222, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_222, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 19398.36
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.06034 0.21585 0.280 0.78
## alpha 0.42894 0.04778 8.978 <2e-16 ***
## A 5.05639 0.23881 21.174 <2e-16 ***
## k 41.09173 2.77384 14.814 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.048 on 4838 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 5.802e-06
## (1003 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 489 rows containing missing values (`geom_point()`).## Warning: Removed 1053 rows containing missing values (`geom_line()`).
plotting 2
223 - Central Interior Broadleaf Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8742 10361
## 2 8741 10217 1 143.46 122.73 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 35815.03
## 2 2 35695.10##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.81212 0.10812 -7.511 6.43e-14 ***
## alpha 0.47384 0.04076 11.626 < 2e-16 ***
## A 6.29354 0.22631 27.809 < 2e-16 ***
## k 62.38730 4.20500 14.836 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.081 on 8741 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 6.781e-06
## (1265 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_223, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_223, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_223, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 35695.1
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.81212 0.10812 -7.511 6.43e-14 ***
## alpha 0.47384 0.04076 11.626 < 2e-16 ***
## A 6.29354 0.22631 27.809 < 2e-16 ***
## k 62.38730 4.20500 14.836 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.081 on 8741 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 6.781e-06
## (1265 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: Removed 620 rows containing missing values (`geom_point()`).## Warning: Removed 1002 rows containing missing values (`geom_line()`).
plotting 2
231 - Southeastern Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 13233 28484
## 2 13232 27214 1 1269.8 617.4 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 66709.26
## 2 2 66107.65##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.05100 0.21819 9.40 <2e-16 ***
## alpha 0.57826 0.02162 26.75 <2e-16 ***
## A 4.03929 0.13015 31.04 <2e-16 ***
## k 12.85831 0.73266 17.55 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.434 on 13232 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 2.169e-06
## (281 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_231, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_231, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_231, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 66107.65
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.05100 0.21819 9.40 <2e-16 ***
## alpha 0.57826 0.02162 26.75 <2e-16 ***
## A 4.03929 0.13015 31.04 <2e-16 ***
## k 12.85831 0.73266 17.55 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.434 on 13232 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 2.169e-06
## (281 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 143 rows containing missing values (`geom_point()`).## Warning: Removed 1017 rows containing missing values (`geom_line()`).
plotting 2
232 - Outer Coastal Plain Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 13303 32383
## 2 13302 31089 1 1294.5 553.9 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 67119.92
## 2 2 66579.08##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.28290 0.19506 6.577 4.98e-11 ***
## alpha 0.55302 0.02165 25.546 < 2e-16 ***
## A 4.56589 0.15267 29.906 < 2e-16 ***
## k 19.58218 0.99760 19.629 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.529 on 13302 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 1.537e-06
## (323 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_232, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_232, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_232, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 66579.08
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.28290 0.19506 6.577 4.98e-11 ***
## alpha 0.55302 0.02165 25.546 < 2e-16 ***
## A 4.56589 0.15267 29.906 < 2e-16 ***
## k 19.58218 0.99760 19.629 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.529 on 13302 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 1.537e-06
## (323 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 169 rows containing missing values (`geom_point()`).## Warning: Removed 931 rows containing missing values (`geom_line()`).
plotting 2
234 - Lower Mississippi Riverine Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1324 3585.5
## 2 1323 3430.0 1 155.55 59.998 1.879e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 6935.007
## 2 2 6878.152##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.38770 0.89916 1.543 0.123
## alpha 0.72003 0.08454 8.517 < 2e-16 ***
## A 4.09026 0.61709 6.628 4.93e-11 ***
## k 12.26760 2.92583 4.193 2.94e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.61 on 1323 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.486e-06
## (61 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_234, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_234, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_234, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 6878.152
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.38770 0.89916 1.543 0.123
## alpha 0.72003 0.08454 8.517 < 2e-16 ***
## A 4.09026 0.61709 6.628 4.93e-11 ***
## k 12.26760 2.92583 4.193 2.94e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.61 on 1323 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.486e-06
## (61 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.91191, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.5683, p-value = 4.916e-06
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 27 rows containing missing values (`geom_point()`).## Warning: Removed 645 rows containing missing values (`geom_line()`).
plotting 2
242 - Pacific Lowland Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 77 134.46
## 2 76 125.84 1 8.6128 5.2016 0.02537 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 422.9552
## 2 2 419.6591##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.1712 2.5098 -0.068 0.9458
## alpha 0.8913 0.3544 2.515 0.0140 *
## A 8.5388 4.6759 1.826 0.0718 .
## k 29.0699 15.4743 1.879 0.0641 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.287 on 76 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 3.566e-06
## (3 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_242, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_242, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_242, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 419.6591
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.1712 2.5098 -0.068 0.9458
## alpha 0.8913 0.3544 2.515 0.0140 *
## A 8.5388 4.6759 1.826 0.0718 .
## k 29.0699 15.4743 1.879 0.0641 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.287 on 76 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 3.566e-06
## (3 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.90404, p-value = 1.812e-05
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 0.28449, p-value = 0.776
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 2 rows containing missing values (`geom_point()`).## Warning: Removed 725 rows containing missing values (`geom_line()`).
plotting 2
251 - Prairie Parkland (Temperate)
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1785 2660.0
## 2 1784 2658.8 1 1.1725 0.7867 0.3752
## model AIC
## 1 1 7632.934
## 2 2 7634.145##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.4527 0.4946 0.915 0.36
## A 3.4709 0.3419 10.151 < 2e-16 ***
## k 24.0566 4.2655 5.640 1.98e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.221 on 1785 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 4.972e-06
## (507 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_251, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_251, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_251, :
## parameters without starting value in 'data': tau
## model AIC
## 1 1 7632.934
## 2 1a NA
## 3 1b NA
## 4 1c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.4527 0.4946 0.915 0.36
## A 3.4709 0.3419 10.151 < 2e-16 ***
## k 24.0566 4.2655 5.640 1.98e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.221 on 1785 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 4.972e-06
## (507 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.69868, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -7.6629, p-value = 1.818e-14
## alternative hypothesis: two.sidedpredict and plot
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: Removed 254 rows containing missing values (`geom_point()`).## Warning: Removed 1176 rows containing missing values (`geom_line()`).
plotting 2
255 - Prairie Parkland (Subtropical)
Model selection 1
## Error in nls(fg_1_MBg5, data = G_255, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2_MBg5, data = G_255, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_255$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_255.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
261 - California Coastal Chaparral Forest and Shrub
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
262 - California Dry Steppe
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
263 - California Coastal Steppe - Mixed Forest and Redwood Forest
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
add p model: does not fit
add s model: does not fit
add s+p model: does not fit
note: model fit, but fit was funky due to data being sparse
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
313 - Colorado Plateau Semi-Desert
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 212 98.175
## 2 211 95.055 1 3.12 6.9257 0.009124 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 485.5008
## 2 2 480.5572##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.2288 0.8965 -1.371 0.17194
## alpha 0.7287 0.2474 2.946 0.00358 **
## A 4.5680 1.4482 3.154 0.00184 **
## k 120.2565 37.7272 3.188 0.00165 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6712 on 211 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 1.419e-06
## (3 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_313, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_313, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_313, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 480.5572
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.2288 0.8965 -1.371 0.17194
## alpha 0.7287 0.2474 2.946 0.00358 **
## A 4.5680 1.4482 3.154 0.00184 **
## k 120.2565 37.7272 3.188 0.00165 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6712 on 211 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 1.419e-06
## (3 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.97747, p-value = 0.001613
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -0.06549, p-value = 0.9478
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1103 rows containing missing values (`geom_line()`).
plotting 2
315 - Southwest Plateau and Plains Dry Steppe and Shrub
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
321 - Chihuahuan Semi-Desert
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
322 - American Semidesert and Desert
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
331 - Great Plains/Palouse Dry Steppe
Model selection 1
## Error in nls(fg_1_MBg5, data = G_331, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2_MBg5, data = G_331, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_331.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
332 - Great Plains Steppe
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 193 155.63
## 2 192 155.46 1 0.16853 0.2081 0.6487
## model AIC
## 1 1 637.9079
## 2 2 639.6956##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.3291 1.3677 0.241 0.81011
## A 4.3422 1.3161 3.299 0.00115 **
## k 74.7556 23.5156 3.179 0.00172 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.898 on 193 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 2.347e-06
## (36 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_332, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_332, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_332, :
## parameters without starting value in 'data': tau
## model AIC
## 1 1 637.9079
## 2 1a NA
## 3 1b NA
## 4 1c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.3291 1.3677 0.241 0.81011
## A 4.3422 1.3161 3.299 0.00115 **
## k 74.7556 23.5156 3.179 0.00172 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.898 on 193 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 2.347e-06
## (36 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.89621, p-value = 1.963e-10
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.2715, p-value = 0.2035
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 18 rows containing missing values (`geom_point()`).## Warning: Removed 1120 rows containing missing values (`geom_line()`).
plotting 2
341 - Intermountain Semi-desert & Desert
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
342 - Intermountain Semi-Desert
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 112 71.868
## 2 111 65.507 1 6.3609 10.778 0.001374 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 293.4221
## 2 2 284.7648##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.873 6.589 0.436 0.663615
## alpha 0.888 0.235 3.779 0.000255 ***
## A 2.919 2.546 1.146 0.254058
## k 90.854 34.235 2.654 0.009126 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7682 on 111 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 7.021e-06
## (9 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_342, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_342, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_342, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 284.7648
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.873 6.589 0.436 0.663615
## alpha 0.888 0.235 3.779 0.000255 ***
## A 2.919 2.546 1.146 0.254058
## k 90.854 34.235 2.654 0.009126 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7682 on 111 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 7.021e-06
## (9 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.94429, p-value = 0.0001199
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -0.94042, p-value = 0.347
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 4 rows containing missing values (`geom_point()`).## Warning: Removed 1241 rows containing missing values (`geom_line()`).
plotting 2
411 - Everglades
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6746 5130.8
## 2 6745 5102.6 1 28.237 37.326 1.055e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 24245.14
## 2 2 24209.89##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.77864 0.28950 6.144 8.51e-10 ***
## alpha 0.20350 0.03252 6.257 4.15e-10 ***
## A 2.99829 0.14210 21.101 < 2e-16 ***
## k 33.06165 1.76977 18.681 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8698 on 6745 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 6.312e-06
## (23 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_M211, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_M211, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_M211, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 24209.89
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.77864 0.28950 6.144 8.51e-10 ***
## alpha 0.20350 0.03252 6.257 4.15e-10 ***
## A 2.99829 0.14210 21.101 < 2e-16 ***
## k 33.06165 1.76977 18.681 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8698 on 6745 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 6.312e-06
## (23 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 14 rows containing missing values (`geom_point()`).## Warning: Removed 1108 rows containing missing values (`geom_line()`).
plotting 2
M221 - Eastern Broadleaf Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8257 15147
## 2 8256 14972 1 174.97 96.483 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 39278.06
## 2 2 39184.09##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.01062 0.15916 0.067 0.947
## alpha 0.56783 0.05558 10.217 <2e-16 ***
## A 4.80611 0.18242 26.347 <2e-16 ***
## k 27.74238 2.78273 9.969 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 8256 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 8.228e-06
## (55 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_M221, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_M221, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_M221, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 39184.09
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.01062 0.15916 0.067 0.947
## alpha 0.56783 0.05558 10.217 <2e-16 ***
## A 4.80611 0.18242 26.347 <2e-16 ***
## k 27.74238 2.78273 9.969 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 8256 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 8.228e-06
## (55 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 27 rows containing missing values (`geom_point()`).## Warning: Removed 982 rows containing missing values (`geom_line()`).
plotting 2
M223 - Ozark Broadleaf Forest Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 887 1241.7
## 2 886 1231.0 1 10.651 7.6657 0.005745 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3673.208
## 2 2 3667.540##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.3199 1.2375 1.875 0.06117 .
## alpha 0.4618 0.1599 2.889 0.00396 **
## A 2.4511 0.4773 5.136 3.46e-07 ***
## k 31.9347 10.1809 3.137 0.00176 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.179 on 886 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 7.832e-06
## (6 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_M223, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_M223, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_M223, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 3667.54
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.3199 1.2375 1.875 0.06117 .
## alpha 0.4618 0.1599 2.889 0.00396 **
## A 2.4511 0.4773 5.136 3.46e-07 ***
## k 31.9347 10.1809 3.137 0.00176 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.179 on 886 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 7.832e-06
## (6 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93319, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.1934, p-value = 0.02828
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 6 rows containing missing values (`geom_point()`).## Warning: Removed 1175 rows containing missing values (`geom_line()`).
plotting 2
M231 - Ouachita Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 989 1325.2
## 2 988 1309.2 1 16.095 12.147 0.0005134 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4081.795
## 2 2 4071.673##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 3.6087 1.7664 2.043 0.041328 *
## alpha 0.3982 0.1098 3.625 0.000303 ***
## A 2.3591 0.5308 4.445 9.81e-06 ***
## k 34.1285 7.1289 4.787 1.95e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.151 on 988 degrees of freedom
##
## Number of iterations to convergence: 11
## Achieved convergence tolerance: 4.638e-06
## (14 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_M231, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_M231, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_M231, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 4071.673
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 3.6087 1.7664 2.043 0.041328 *
## alpha 0.3982 0.1098 3.625 0.000303 ***
## A 2.3591 0.5308 4.445 9.81e-06 ***
## k 34.1285 7.1289 4.787 1.95e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.151 on 988 degrees of freedom
##
## Number of iterations to convergence: 11
## Achieved convergence tolerance: 4.638e-06
## (14 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93428, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.4777, p-value = 7.547e-06
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 7 rows containing missing values (`geom_point()`).## Warning: Removed 1218 rows containing missing values (`geom_line()`).
plotting 2
M242 - Cascade Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3147 8391.5
## 2 3146 8049.3 1 342.15 133.72 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 16065.44
## 2 2 15936.31##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.69672 0.23864 -7.11 1.43e-12 ***
## alpha 0.90419 0.07101 12.73 < 2e-16 ***
## A 13.03876 1.08531 12.01 < 2e-16 ***
## k 140.11490 11.12055 12.60 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.6 on 3146 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 7.442e-06
## (74 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_M242, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_M242, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_M242, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 15936.31
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.69672 0.23864 -7.11 1.43e-12 ***
## alpha 0.90419 0.07101 12.73 < 2e-16 ***
## A 13.03876 1.08531 12.01 < 2e-16 ***
## k 140.11490 11.12055 12.60 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.6 on 3146 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 7.442e-06
## (74 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.9273, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.7199, p-value = 2.36e-06
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 39 rows containing missing values (`geom_point()`).## Warning: Removed 126 rows containing missing values (`geom_line()`).
plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
Model selection 1
## Error in as.formula(formula) : object 'fg_3_MBg5' not found
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1682 3596.1
## 2 1681 3529.9 1 66.205 31.528 2.296e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 7923.648
## 2 2 7894.338##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.5662 0.3694 -4.240 2.35e-05 ***
## alpha 0.6405 0.1065 6.017 2.18e-09 ***
## A 15.1529 1.8631 8.133 8.04e-16 ***
## k 247.1243 29.7556 8.305 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.449 on 1681 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 6.227e-06
## (292 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_M261, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_M261, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_M261, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 7894.338
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.5662 0.3694 -4.240 2.35e-05 ***
## alpha 0.6405 0.1065 6.017 2.18e-09 ***
## A 15.1529 1.8631 8.133 8.04e-16 ***
## k 247.1243 29.7556 8.305 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.449 on 1681 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 6.227e-06
## (292 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.89716, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.1811, p-value = 0.02917
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 155 rows containing missing values (`geom_point()`).
plotting 2
M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 363 164.19
## 2 362 155.26 1 8.9317 20.825 6.902e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 863.6187
## 2 2 845.1470##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -2.1151 0.3241 -6.526 2.28e-10 ***
## alpha 0.5769 0.1140 5.061 6.64e-07 ***
## A 9.0519 1.6558 5.467 8.55e-08 ***
## k 153.8173 35.7964 4.297 2.23e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6549 on 362 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.706e-06
## (1 observation deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_M313, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_M313, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_M313, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 845.147
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -2.1151 0.3241 -6.526 2.28e-10 ***
## alpha 0.5769 0.1140 5.061 6.64e-07 ***
## A 9.0519 1.6558 5.467 8.55e-08 ***
## k 153.8173 35.7964 4.297 2.23e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6549 on 362 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.706e-06
## (1 observation deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.96978, p-value = 6.75e-07
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 0.20111, p-value = 0.8406
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1183 rows containing missing values (`geom_line()`).
plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1732 1437.1
## 2 1731 1365.6 1 71.524 90.662 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4817.306
## 2 2 4730.734##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.70648 0.56744 -1.245 0.213
## alpha 0.61261 0.05552 11.034 < 2e-16 ***
## A 2.71538 0.41186 6.593 5.71e-11 ***
## k 49.20052 7.28457 6.754 1.96e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8882 on 1731 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 5.632e-06
## (21 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_M331, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_M331, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_M331, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 4730.734
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.70648 0.56744 -1.245 0.213
## alpha 0.61261 0.05552 11.034 < 2e-16 ***
## A 2.71538 0.41186 6.593 5.71e-11 ***
## k 49.20052 7.28457 6.754 1.96e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8882 on 1731 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 5.632e-06
## (21 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.84462, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -5.8623, p-value = 4.565e-09
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 7 rows containing missing values (`geom_point()`).## Warning: Removed 1091 rows containing missing values (`geom_line()`).
plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2513 2563.7
## 2 2512 2382.2 1 181.47 191.35 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 8302.884
## 2 2 8120.177##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.85974 0.41763 -2.059 0.0396 *
## alpha 0.77968 0.04943 15.773 < 2e-16 ***
## A 5.26625 0.64151 8.209 3.52e-16 ***
## k 87.68462 9.15787 9.575 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9738 on 2512 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 8.659e-06
## (96 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_M332, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_M332, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_M332, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 8120.177
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.85974 0.41763 -2.059 0.0396 *
## alpha 0.77968 0.04943 15.773 < 2e-16 ***
## A 5.26625 0.64151 8.209 3.52e-16 ***
## k 87.68462 9.15787 9.575 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9738 on 2512 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 8.659e-06
## (96 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.90151, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -6.5103, p-value = 7.503e-11
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 46 rows containing missing values (`geom_point()`).## Warning: Removed 1001 rows containing missing values (`geom_line()`).
plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1691 2038.0
## 2 1690 1822.6 1 215.4 199.74 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 6585.713
## 2 2 6398.480##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.33295 0.64045 -0.520 0.603
## alpha 0.85976 0.05366 16.022 < 2e-16 ***
## A 5.78213 0.89533 6.458 1.38e-10 ***
## k 61.15069 6.48496 9.430 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.038 on 1690 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 9.562e-06
## (59 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_M333, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_M333, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_M333, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 6398.48
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.33295 0.64045 -0.520 0.603
## alpha 0.85976 0.05366 16.022 < 2e-16 ***
## A 5.78213 0.89533 6.458 1.38e-10 ***
## k 61.15069 6.48496 9.430 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.038 on 1690 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 9.562e-06
## (59 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.92681, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -5.6822, p-value = 1.329e-08
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 29 rows containing missing values (`geom_point()`).## Warning: Removed 925 rows containing missing values (`geom_line()`).
plotting 2
M334 - Black Hills Coniferous Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 355 329.22
## 2 354 306.86 1 22.366 25.802 6.132e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 1055.669
## 2 2 1032.482##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.7785 0.8475 -0.919 0.35894
## alpha 0.7622 0.1330 5.731 2.14e-08 ***
## A 2.9995 0.6721 4.463 1.09e-05 ***
## k 37.6314 11.3745 3.308 0.00103 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.931 on 354 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 2.618e-06
## (101 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_MBg5", sep = "")), data = G_M334, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_MBg5", sep = "")), data = G_M334, :
## parameters without starting value in 'data': tau
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_MBg5", sep = "")), data = G_M334, :
## parameters without starting value in 'data': tau
## model AIC
## 1 2 1032.482
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.7785 0.8475 -0.919 0.35894
## alpha 0.7622 0.1330 5.731 2.14e-08 ***
## A 2.9995 0.6721 4.463 1.09e-05 ***
## k 37.6314 11.3745 3.308 0.00103 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.931 on 354 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 2.618e-06
## (101 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.82141, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.998, p-value = 0.04572
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 48 rows containing missing values (`geom_point()`).## Warning: Removed 1264 rows containing missing values (`geom_line()`).
plotting 2
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 2 |
| 212 | Laurentian Mixed Forest | 2 |
| 221 | Eastern Broadleaf Forest | 2 |
| 222 | Midwest Broadleaf Forest | 2 |
| 223 | Central Interior Broadleaf Forest | 2 |
| 231 | Southeastern Mixed Forest | 2 |
| 232 | Outer Coastal Plain Mixed Forest | 2 |
| 234 | Lower Mississippi Riverine Forest | 2 |
| 242 | Pacific Lowland Mixed Forest | 2 |
| 251 | Prairie Parkland (Temperate) | 1 |
| 255 | Prairie Parkland (Subtropical) | NA |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | NA |
| 313 | Colorado Plateau Semi-Desert | 2 |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | NA |
| 332 | Great Plains Steppe | 1 |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | 2 |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 2 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 2 |
| M223 | Ozark Broadleaf Forest Meadow | 2 |
| M231 | Ouachita Mixed Forest | 2 |
| M242 | Cascade Mixed Forest | 2 |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 2 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | 2 |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | 2 |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | 2 |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | 2 |
| M334 | Black Hills Coniferous Forest | 2 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | NA |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | tau | tau.variance | tau.2.5 | tau.97.5 | alpha | alpha.variance | alpha.2.5 | alpha.97.5 | A | A.2.5 | A.97.5 | k | k.2.5 | k.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 6877 | 2876 | 1.0226050 | 0.0519878 | 0.5756375 | 1.4695724 | 0.1514785 | 0.0014519 | 0.0767828 | 0.2261741 | 3.587040 | 3.2949297 | 3.879151 | 32.13641 | 28.711930 | 35.56088 |
| 212 | Laurentian Mixed Forest | east | 22715 | 9499 | 1.3387330 | 0.0335010 | 0.9799723 | 1.6974936 | 0.2731086 | 0.0006585 | 0.2228094 | 0.3234078 | 3.215390 | 3.0216746 | 3.409105 | 43.84726 | 40.923499 | 46.77103 |
| 221 | Eastern Broadleaf Forest | east | 7333 | 3571 | -0.8602079 | 0.0142148 | -1.0939255 | -0.6264904 | 0.5232389 | 0.0015948 | 0.4449551 | 0.6015226 | 5.932320 | 5.5348903 | 6.329749 | 32.54201 | 27.493976 | 37.59004 |
| 222 | Midwest Broadleaf Forest | east | 5845 | 2589 | 0.0603405 | 0.0465895 | -0.3628157 | 0.4834966 | 0.4289446 | 0.0022828 | 0.3352762 | 0.5226130 | 5.056391 | 4.5882249 | 5.524558 | 41.09173 | 35.653746 | 46.52972 |
| 223 | Central Interior Broadleaf Forest | east | 10010 | 3864 | -0.8121234 | 0.0116899 | -1.0240639 | -0.6001829 | 0.4738433 | 0.0016610 | 0.3939523 | 0.5537342 | 6.293537 | 5.8499174 | 6.737157 | 62.38730 | 54.144505 | 70.63010 |
| 231 | Southeastern Mixed Forest | east | 13517 | 6193 | 2.0510032 | 0.0476053 | 1.6233265 | 2.4786798 | 0.5782563 | 0.0004673 | 0.5358818 | 0.6206308 | 4.039289 | 3.7841680 | 4.294410 | 12.85831 | 11.422197 | 14.29443 |
| 232 | Outer Coastal Plain Mixed Forest | east | 13629 | 6626 | 1.2829040 | 0.0380496 | 0.9005528 | 1.6652552 | 0.5530174 | 0.0004686 | 0.5105848 | 0.5954501 | 4.565889 | 4.2666295 | 4.865149 | 19.58218 | 17.626745 | 21.53761 |
| 234 | Lower Mississippi Riverine Forest | east | 1388 | 778 | 1.3876959 | 0.8084870 | -0.3762372 | 3.1516290 | 0.7200306 | 0.0071467 | 0.5541876 | 0.8858735 | 4.090264 | 2.8796789 | 5.300849 | 12.26760 | 6.527838 | 18.00737 |
| 242 | Pacific Lowland Mixed Forest | pacific | 83 | 83 | -0.1711992 | 6.2989162 | -5.1698277 | 4.8274294 | 0.8912680 | 0.1256302 | 0.1853325 | 1.5972035 | 8.538822 | -0.7741012 | 17.851745 | 29.06994 | -1.749796 | 59.88967 |
| 251 | Prairie Parkland (Temperate) | east | 2295 | 906 | 0.4526785 | 0.2446043 | -0.5173282 | 1.4226853 | NA | NA | NA | NA | 3.470867 | 2.8002520 | 4.141481 | 24.05661 | 15.690636 | 32.42259 |
| 255 | Prairie Parkland (Subtropical) | east | 717 | 319 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 261 | California Coastal Chaparral Forest and Shrub | pacific | 25 | 25 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | pacific | 163 | 161 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 313 | Colorado Plateau Semi-Desert | interior west | 218 | 218 | -1.2288065 | 0.8037346 | -2.9960752 | 0.5384623 | 0.7286917 | 0.0611899 | 0.2410667 | 1.2163167 | 4.567967 | 1.7131691 | 7.422764 | 120.25652 | 45.885956 | 194.62709 |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | interior west | 4 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | interior west | 9 | 9 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | interior west | 3 | 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | interior west | 331 | 255 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 332 | Great Plains Steppe | interior west | 232 | 128 | 0.3290914 | 1.8705883 | -2.3684559 | 3.0266386 | NA | NA | NA | NA | 4.342163 | 1.7462960 | 6.938029 | 74.75556 | 28.375089 | 121.13604 |
| 341 | Intermountain Semi-Desert and Desert | interior west | 66 | 64 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | interior west | 124 | 123 | 2.8735167 | 43.4175637 | -10.1834240 | 15.9304575 | 0.8880369 | 0.0552257 | 0.4223658 | 1.3537080 | 2.919332 | -2.1263725 | 7.965035 | 90.85407 | 23.015368 | 158.69278 |
| 411 | Everglades | east | 96 | 63 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 6772 | 3006 | 1.7786421 | 0.0838090 | 1.2111348 | 2.3461494 | 0.2035009 | 0.0010577 | 0.1397484 | 0.2672535 | 2.998289 | 2.7197370 | 3.276841 | 33.06165 | 29.592339 | 36.53096 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 8315 | 3810 | 0.0106157 | 0.0253307 | -0.3013705 | 0.3226019 | 0.5678328 | 0.0030888 | 0.4588878 | 0.6767779 | 4.806111 | 4.4485294 | 5.163693 | 27.74238 | 22.287527 | 33.19724 |
| M223 | Ozark Broadleaf Forest Meadow | east | 896 | 349 | 2.3198982 | 1.5314664 | -0.1089228 | 4.7487192 | 0.4618123 | 0.0255588 | 0.1480418 | 0.7755827 | 2.451102 | 1.5143755 | 3.387828 | 31.93472 | 11.953289 | 51.91616 |
| M231 | Ouachita Mixed Forest | east | 1006 | 495 | 3.6086566 | 3.1203260 | 0.1422427 | 7.0750706 | 0.3981807 | 0.0120643 | 0.1826393 | 0.6137221 | 2.359071 | 1.3174800 | 3.400662 | 34.12849 | 20.138981 | 48.11800 |
| M242 | Cascade Mixed Forest | pacific | 3224 | 3207 | -1.6967250 | 0.0569485 | -2.1646285 | -1.2288214 | 0.9041933 | 0.0050426 | 0.7649596 | 1.0434271 | 13.038763 | 10.9107794 | 15.166746 | 140.11490 | 118.310628 | 161.91917 |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | pacific | 1977 | 1807 | -1.5662388 | 0.1364314 | -2.2907049 | -0.8417727 | 0.6405465 | 0.0113330 | 0.4317453 | 0.8493478 | 15.152926 | 11.4986698 | 18.807181 | 247.12429 | 188.762394 | 305.48619 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | interior west | 30 | 26 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 367 | 367 | -2.1151193 | 0.1050385 | -2.7524676 | -1.4777710 | 0.5769339 | 0.0129929 | 0.3527748 | 0.8010930 | 9.051883 | 5.7957703 | 12.307996 | 153.81728 | 83.422344 | 224.21222 |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 1756 | 1756 | -0.7064804 | 0.3219889 | -1.8194218 | 0.4064611 | 0.6126066 | 0.0030822 | 0.5037180 | 0.7214952 | 2.715381 | 1.9075824 | 3.523179 | 49.20052 | 34.913026 | 63.48802 |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 2612 | 2602 | -0.8597398 | 0.1744170 | -1.6786792 | -0.0408004 | 0.7796848 | 0.0024433 | 0.6827569 | 0.8766126 | 5.266247 | 4.0083136 | 6.524181 | 87.68462 | 69.726871 | 105.64238 |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 1753 | 1742 | -0.3329468 | 0.4101749 | -1.5891034 | 0.9232097 | 0.8597628 | 0.0028795 | 0.7545146 | 0.9650110 | 5.782128 | 4.0260469 | 7.538210 | 61.15069 | 48.431305 | 73.87008 |
| M334 | Black Hills Coniferous Forest | interior west | 459 | 181 | -0.7784535 | 0.7181787 | -2.4451315 | 0.8882244 | 0.7621556 | 0.0176865 | 0.5006044 | 1.0237068 | 2.999537 | 1.6777117 | 4.321362 | 37.63137 | 15.261203 | 60.00153 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | interior west | 220 | 220 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot tau
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I## Warning: package 'ggnewscale' was built under R version 4.2.1## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot alpha (biomass growth compensation effect)
plot A (asymptote of forest biomass growth in Mg/ha/yr)
## Warning: Removed 12 rows containing missing values (`geom_point()`).
plot k (stand biomass at half biomss G in Mg/ha)
## Warning: Removed 12 rows containing missing values (`geom_point()`).
Caclulations - weighted averages
tau (productivity trend (in %) 2000-2021)
## region weighted.tau weighted.tau.std_Error 95 % CI, upper
## 1 entire US 0.46864470 0.06827335 0.60246046
## 2 pacific -0.14304359 0.01789591 -0.10796761
## 3 east 0.69536681 0.05562962 0.80440087
## 4 interior west -0.08367852 0.03530342 -0.01448381
## 95 % CI, lower
## 1 0.3348289
## 2 -0.1781196
## 3 0.5863327
## 4 -0.1528732alpha (biomass growth compensation effect)
## region weighted.alpha weighted.alpha.std_Error 95 % CI, upper
## 1 entire US 0.48562909 1.802426e-07 0.48562944
## 2 pacific 0.07131965 5.171433e-03 0.08145566
## 3 east 0.32419373 8.371647e-03 0.34060216
## 4 interior west 0.09011570 3.489543e-03 0.09695521
## 95 % CI, lower
## 1 0.48562873
## 2 0.06118364
## 3 0.30778530
## 4 0.08327620A (asymptote of forest biomass growth in Mg/ha/yr)
## region weighted.A
## 1 entire US 5.086239
## 2 pacific 13.232135
## 3 east 4.236292
## 4 interior west 4.458184K (stand biomass at half biomass G in Mg/ha)
## region weighted.k
## 1 entire US 49.65117
## 2 pacific 170.03878
## 3 east 32.18296
## 4 interior west 69.01766Analaysis 2: \(G_{TI-NoIngrow}\)
211 - Northeastern Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6822 4506.3
## 2 6821 4178.5 1 327.82 535.14 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 21450.25
## 2 2 20936.76##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.55605 0.18380 3.025 0.00249 **
## alpha 0.79613 0.03177 25.061 < 2e-16 ***
## A 4.92563 0.20624 23.884 < 2e-16 ***
## k 113.43273 5.37133 21.118 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7827 on 6821 degrees of freedom
##
## Number of iterations to convergence: 4
## Achieved convergence tolerance: 5.58e-06
## (52 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_211, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_211, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_211, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 20936.76
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.55605 0.18380 3.025 0.00249 **
## alpha 0.79613 0.03177 25.061 < 2e-16 ***
## A 4.92563 0.20624 23.884 < 2e-16 ***
## k 113.43273 5.37133 21.118 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7827 on 6821 degrees of freedom
##
## Number of iterations to convergence: 4
## Achieved convergence tolerance: 5.58e-06
## (52 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 22 rows containing missing values (`geom_point()`).## Warning: Removed 1038 rows containing missing values (`geom_line()`).
plotting 2
212 - Laurentian Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 18911 11656
## 2 18910 10322 1 1333.6 2443.1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 47432.75
## 2 2 45136.56##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.15700 0.15150 7.637 2.33e-14 ***
## alpha 1.06939 0.01945 54.995 < 2e-16 ***
## A 5.57364 0.19898 28.012 < 2e-16 ***
## k 213.12553 8.12030 26.246 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7388 on 18910 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 2.389e-06
## (3801 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_212, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_212, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_212, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 45136.56
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.15700 0.15150 7.637 2.33e-14 ***
## alpha 1.06939 0.01945 54.995 < 2e-16 ***
## A 5.57364 0.19898 28.012 < 2e-16 ***
## k 213.12553 8.12030 26.246 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7388 on 18910 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 2.389e-06
## (3801 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 1880 rows containing missing values (`geom_point()`).## Warning: Removed 1031 rows containing missing values (`geom_line()`).
plotting 2
221 - Eastern Broadleaf Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 7266 7882.2
## 2 7265 7425.5 1 456.7 446.83 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 28950.11
## 2 2 28518.25##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.93025 0.11070 -8.404 <2e-16 ***
## alpha 0.82261 0.03629 22.666 <2e-16 ***
## A 7.02241 0.27128 25.886 <2e-16 ***
## k 89.63813 5.69014 15.753 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 7265 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 8.389e-06
## (64 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_221, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_221, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_221, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 28518.25
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.93025 0.11070 -8.404 <2e-16 ***
## alpha 0.82261 0.03629 22.666 <2e-16 ***
## A 7.02241 0.27128 25.886 <2e-16 ***
## k 89.63813 5.69014 15.753 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 7265 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 8.389e-06
## (64 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 29 rows containing missing values (`geom_point()`).## Warning: Removed 1036 rows containing missing values (`geom_line()`).
plotting 2
222 - Midwest Broadleaf Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4839 3647.5
## 2 4838 3294.3 1 353.26 518.8 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 15860.31
## 2 2 15369.08##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.2268 0.2059 1.102 0.271
## alpha 0.9716 0.0384 25.303 <2e-16 ***
## A 6.8417 0.3634 18.829 <2e-16 ***
## k 141.3510 8.6834 16.278 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8252 on 4838 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 6.595e-07
## (1003 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_222, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_222, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_222, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 15369.08
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.2268 0.2059 1.102 0.271
## alpha 0.9716 0.0384 25.303 <2e-16 ***
## A 6.8417 0.3634 18.829 <2e-16 ***
## k 141.3510 8.6834 16.278 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8252 on 4838 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 6.595e-07
## (1003 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 530 rows containing missing values (`geom_point()`).## Warning: Removed 1053 rows containing missing values (`geom_line()`).
plotting 2
223 - Central Interior Broadleaf Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8742 7835.7
## 2 8741 7412.5 1 423.24 499.09 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 31017.79
## 2 2 30534.21##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.98667 0.09422 -10.47 <2e-16 ***
## alpha 0.84872 0.03506 24.21 <2e-16 ***
## A 11.86883 0.67873 17.49 <2e-16 ***
## k 263.34595 19.70084 13.37 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9209 on 8741 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 2.834e-06
## (1265 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_223, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_223, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_223, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 30534.21
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.98667 0.09422 -10.47 <2e-16 ***
## alpha 0.84872 0.03506 24.21 <2e-16 ***
## A 11.86883 0.67873 17.49 <2e-16 ***
## k 263.34595 19.70084 13.37 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9209 on 8741 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 2.834e-06
## (1265 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 628 rows containing missing values (`geom_point()`).## Warning: Removed 1002 rows containing missing values (`geom_line()`).
plotting 2
231 - Southeastern Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 13233 26261
## 2 13232 23406 1 2855.4 1614.2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 60936.50
## 2 2 59414.92##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.55319 0.19646 7.906 2.87e-15 ***
## alpha 0.93883 0.02094 44.825 < 2e-16 ***
## A 5.08782 0.17961 28.327 < 2e-16 ***
## k 62.69065 2.80418 22.356 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.33 on 13232 degrees of freedom
##
## Number of iterations to convergence: 4
## Achieved convergence tolerance: 5.568e-06
## (281 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_231, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_231, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_231, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 59414.92
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.55319 0.19646 7.906 2.87e-15 ***
## alpha 0.93883 0.02094 44.825 < 2e-16 ***
## A 5.08782 0.17961 28.327 < 2e-16 ***
## k 62.69065 2.80418 22.356 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.33 on 13232 degrees of freedom
##
## Number of iterations to convergence: 4
## Achieved convergence tolerance: 5.568e-06
## (281 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 139 rows containing missing values (`geom_point()`).## Warning: Removed 1017 rows containing missing values (`geom_line()`).
plotting 2
232 - Outer Coastal Plain Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 13303 28896
## 2 13302 25620 1 3276.2 1701 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 60049.81
## 2 2 58450.60##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.10660 0.19007 5.822 5.95e-09 ***
## alpha 0.96245 0.02054 46.846 < 2e-16 ***
## A 5.28979 0.19504 27.121 < 2e-16 ***
## k 68.64871 3.00260 22.863 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 13302 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 9.537e-07
## (323 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_232, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_232, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_232, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 58450.6
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.10660 0.19007 5.822 5.95e-09 ***
## alpha 0.96245 0.02054 46.846 < 2e-16 ***
## A 5.28979 0.19504 27.121 < 2e-16 ***
## k 68.64871 3.00260 22.863 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 13302 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 9.537e-07
## (323 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 178 rows containing missing values (`geom_point()`).## Warning: Removed 931 rows containing missing values (`geom_line()`).
plotting 2
234 - Lower Mississippi Riverine Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1324 3905.3
## 2 1323 3650.9 1 254.34 92.167 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 6664.563
## 2 2 6577.195##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.21590 1.31257 1.688 0.0916 .
## alpha 0.98846 0.09115 10.845 < 2e-16 ***
## A 4.12529 0.82457 5.003 6.40e-07 ***
## k 56.92517 10.41403 5.466 5.49e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.661 on 1323 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 4.056e-07
## (61 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_234, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_234, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_234, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 6577.195
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.21590 1.31257 1.688 0.0916 .
## alpha 0.98846 0.09115 10.845 < 2e-16 ***
## A 4.12529 0.82457 5.003 6.40e-07 ***
## k 56.92517 10.41403 5.466 5.49e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.661 on 1323 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 4.056e-07
## (61 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.84539, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.2555, p-value = 0.001132
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 31 rows containing missing values (`geom_point()`).## Warning: Removed 645 rows containing missing values (`geom_line()`).
plotting 2
242 - Pacific Lowland Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 77 64.151
## 2 76 59.090 1 5.0607 6.509 0.01274 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 340.6367
## 2 2 336.0628##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.6580 3.7292 0.445 0.65787
## alpha 0.7566 0.2720 2.782 0.00682 **
## A 6.2469 3.6800 1.698 0.09368 .
## k 90.6221 27.7195 3.269 0.00162 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8818 on 76 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 1.05e-06
## (3 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_242, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_242, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_242, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 336.0628
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.6580 3.7292 0.445 0.65787
## alpha 0.7566 0.2720 2.782 0.00682 **
## A 6.2469 3.6800 1.698 0.09368 .
## k 90.6221 27.7195 3.269 0.00162 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8818 on 76 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 1.05e-06
## (3 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.98469, p-value = 0.4569
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 0.14291, p-value = 0.8864
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1 rows containing missing values (`geom_point()`).## Warning: Removed 725 rows containing missing values (`geom_line()`).
plotting 2
251 - Prairie Parkland (Temperate)
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1785 1329.7
## 2 1784 1305.5 1 24.104 32.937 1.116e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 5834.037
## 2 2 5803.328##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.68113 0.42631 1.598 0.11
## alpha 0.54867 0.09052 6.062 1.64e-09 ***
## A 4.54602 0.44240 10.276 < 2e-16 ***
## k 98.08080 11.90142 8.241 3.27e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8555 on 1784 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 4.188e-06
## (507 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_251, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_251, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_251, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 5803.328
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.68113 0.42631 1.598 0.11
## alpha 0.54867 0.09052 6.062 1.64e-09 ***
## A 4.54602 0.44240 10.276 < 2e-16 ***
## k 98.08080 11.90142 8.241 3.27e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8555 on 1784 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 4.188e-06
## (507 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.88257, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -8.7257, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 276 rows containing missing values (`geom_point()`).## Warning: Removed 1176 rows containing missing values (`geom_line()`).
plotting 2
255 - Prairie Parkland (Subtropical)
Model selection 1
## Error in nls(fg_1_TI, data = G_255, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2_TI, data = G_255, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_255$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_255.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
261 - California Coastal Chaparral Forest and Shrub
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
262 - California Dry Steppe
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
263 - California Coastal Steppe - Mixed Forest and Redwood Forest
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
add p model: does not fit
add s model: does not fit
add s+p model: does not fit
note: model fit, but fit was funky due to data being sparse
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
313 - Colorado Plateau Semi-Desert
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 212 92.785
## 2 211 87.782 1 5.0027 12.025 0.0006362 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 433.8518
## 2 2 423.9356##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.7114 1.2406 -0.573 0.56699
## alpha 0.9558 0.2401 3.981 9.45e-05 ***
## A 4.5484 1.7705 2.569 0.01089 *
## k 189.5822 67.5160 2.808 0.00545 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.645 on 211 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 8.574e-06
## (3 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_313, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_313, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_313, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 423.9356
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.7114 1.2406 -0.573 0.56699
## alpha 0.9558 0.2401 3.981 9.45e-05 ***
## A 4.5484 1.7705 2.569 0.01089 *
## k 189.5822 67.5160 2.808 0.00545 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.645 on 211 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 8.574e-06
## (3 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.98376, p-value = 0.01428
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 0.18459, p-value = 0.8536
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1 rows containing missing values (`geom_point()`).## Warning: Removed 1103 rows containing missing values (`geom_line()`).
plotting 2
315 - Southwest Plateau and Plains Dry Steppe and Shrub
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
321 - Chihuahuan Semi-Desert
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
322 - American Semidesert and Desert
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
331 - Great Plains/Palouse Dry Steppe
Model selection 1
## Error in nls(fg_1_TI, data = G_331, start = c(tau = tau.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2_TI, data = G_331, start = c(tau = tau.start, alpha = alpha.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA## Warning in min(AIC1_331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_331.' not foundsummary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
332 - Great Plains Steppe
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 193 138.02
## 2 192 132.25 1 5.7715 8.3793 0.004235 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 536.3344
## 2 2 529.9619##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.7278 2.2867 0.756 0.45083
## alpha 0.7986 0.2488 3.210 0.00156 **
## A 4.2036 1.6950 2.480 0.01400 *
## k 144.4134 49.0241 2.946 0.00362 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8299 on 192 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 3.776e-06
## (36 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_332, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_332, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_332, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 529.9619
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.7278 2.2867 0.756 0.45083
## alpha 0.7986 0.2488 3.210 0.00156 **
## A 4.2036 1.6950 2.480 0.01400 *
## k 144.4134 49.0241 2.946 0.00362 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8299 on 192 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 3.776e-06
## (36 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.81834, p-value = 2.286e-14
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.666, p-value = 0.007676
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 14 rows containing missing values (`geom_point()`).## Warning: Removed 1120 rows containing missing values (`geom_line()`).
plotting 2
341 - Intermountain Semi-desert & Desert
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
342 - Intermountain Semi-Desert
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 112 56.909
## 2 111 48.388 1 8.5209 19.547 2.299e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 240.3323
## 2 2 223.6793##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.0857 3.4942 0.311 0.7566
## alpha 1.0513 0.2047 5.135 1.22e-06 ***
## A 5.1810 3.3797 1.533 0.1281
## k 176.6826 71.2802 2.479 0.0147 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6602 on 111 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 9.638e-06
## (9 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_342, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_342, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_342, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 223.6793
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.0857 3.4942 0.311 0.7566
## alpha 1.0513 0.2047 5.135 1.22e-06 ***
## A 5.1810 3.3797 1.533 0.1281
## k 176.6826 71.2802 2.479 0.0147 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6602 on 111 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 9.638e-06
## (9 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.90944, p-value = 9.729e-07
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.2102, p-value = 0.2262
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 5 rows containing missing values (`geom_point()`).## Warning: Removed 1241 rows containing missing values (`geom_line()`).
plotting 2
411 - Everglades
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6746 4413.5
## 2 6745 4037.5 1 376 628.14 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 20675.75
## 2 2 20076.80##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.37896 0.26771 5.151 2.67e-07 ***
## alpha 0.81396 0.02971 27.393 < 2e-16 ***
## A 3.71986 0.19227 19.347 < 2e-16 ***
## k 104.43455 5.37051 19.446 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7737 on 6745 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 2.142e-06
## (23 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_M211, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_M211, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_M211, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 20076.8
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.37896 0.26771 5.151 2.67e-07 ***
## alpha 0.81396 0.02971 27.393 < 2e-16 ***
## A 3.71986 0.19227 19.347 < 2e-16 ***
## k 104.43455 5.37051 19.446 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7737 on 6745 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 2.142e-06
## (23 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 15 rows containing missing values (`geom_point()`).## Warning: Removed 1108 rows containing missing values (`geom_line()`).
plotting 2
M221 - Eastern Broadleaf Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8257 12696
## 2 8256 12324 1 371.97 249.19 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 36024.31
## 2 2 35780.69##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.31033 0.17766 1.747 0.0807 .
## alpha 0.88596 0.05292 16.743 <2e-16 ***
## A 5.64956 0.27514 20.533 <2e-16 ***
## k 105.48995 7.91642 13.325 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.222 on 8256 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 1.972e-06
## (55 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_M221, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_M221, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_M221, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 35780.69
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.31033 0.17766 1.747 0.0807 .
## alpha 0.88596 0.05292 16.743 <2e-16 ***
## A 5.64956 0.27514 20.533 <2e-16 ***
## k 105.48995 7.91642 13.325 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.222 on 8256 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 1.972e-06
## (55 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 22 rows containing missing values (`geom_point()`).## Warning: Removed 982 rows containing missing values (`geom_line()`).
plotting 2
M223 - Ozark Broadleaf Forest Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 887 1168.5
## 2 886 1122.8 1 45.629 36.005 2.866e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3366.802
## 2 2 3333.350##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 3.5517 1.8231 1.948 0.051715 .
## alpha 1.0187 0.1552 6.563 8.95e-11 ***
## A 2.7562 0.7427 3.711 0.000219 ***
## k 114.7022 32.6123 3.517 0.000458 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.126 on 886 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 4.074e-06
## (6 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_M223, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_M223, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_M223, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 3333.35
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 3.5517 1.8231 1.948 0.051715 .
## alpha 1.0187 0.1552 6.563 8.95e-11 ***
## A 2.7562 0.7427 3.711 0.000219 ***
## k 114.7022 32.6123 3.517 0.000458 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.126 on 886 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 4.074e-06
## (6 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.95654, p-value = 1.414e-15
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.0727, p-value = 0.2834
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 4 rows containing missing values (`geom_point()`).## Warning: Removed 1175 rows containing missing values (`geom_line()`).
plotting 2
M231 - Ouachita Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 989 1289.9
## 2 988 1209.2 1 80.707 65.945 1.373e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3745.090
## 2 2 3682.993##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 5.0159 2.5882 1.938 0.052908 .
## alpha 0.9369 0.1062 8.821 < 2e-16 ***
## A 2.7200 0.8084 3.365 0.000796 ***
## k 114.4827 24.7841 4.619 4.36e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.106 on 988 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 2.507e-06
## (14 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_M231, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_M231, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_M231, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 3682.993
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 5.0159 2.5882 1.938 0.052908 .
## alpha 0.9369 0.1062 8.821 < 2e-16 ***
## A 2.7200 0.8084 3.365 0.000796 ***
## k 114.4827 24.7841 4.619 4.36e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.106 on 988 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 2.507e-06
## (14 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93022, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.4269, p-value = 9.56e-06
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 4 rows containing missing values (`geom_point()`).## Warning: Removed 1218 rows containing missing values (`geom_line()`).
plotting 2
M242 - Cascade Mixed Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3147 6340.4
## 2 3146 5972.5 1 367.87 193.78 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 14546.02
## 2 2 14359.74##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.73500 0.21775 -7.968 2.24e-15 ***
## alpha 1.01162 0.06536 15.477 < 2e-16 ***
## A 11.13446 0.85343 13.047 < 2e-16 ***
## k 136.14898 9.82982 13.851 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 3146 degrees of freedom
##
## Number of iterations to convergence: 4
## Achieved convergence tolerance: 3.142e-06
## (74 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_M242, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_M242, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_M242, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 14359.74
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.73500 0.21775 -7.968 2.24e-15 ***
## alpha 1.01162 0.06536 15.477 < 2e-16 ***
## A 11.13446 0.85343 13.047 < 2e-16 ***
## k 136.14898 9.82982 13.851 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 3146 degrees of freedom
##
## Number of iterations to convergence: 4
## Achieved convergence tolerance: 3.142e-06
## (74 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.90075, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -5.1248, p-value = 2.978e-07
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 46 rows containing missing values (`geom_point()`).## Warning: Removed 126 rows containing missing values (`geom_line()`).
plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1682 2218.6
## 2 1681 2092.8 1 125.76 101.02 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 6757.119
## 2 2 6660.790##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.15923 0.38534 -3.008 0.00267 **
## alpha 0.92459 0.08357 11.063 < 2e-16 ***
## A 12.07063 1.35120 8.933 < 2e-16 ***
## k 258.31415 25.56496 10.104 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1681 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 3.359e-07
## (292 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_M261, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_M261, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_M261, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 6660.79
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.15923 0.38534 -3.008 0.00267 **
## alpha 0.92459 0.08357 11.063 < 2e-16 ***
## A 12.07063 1.35120 8.933 < 2e-16 ***
## k 258.31415 25.56496 10.104 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1681 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 3.359e-07
## (292 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.90309, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.6679, p-value = 0.007632
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 154 rows containing missing values (`geom_point()`).
plotting 2
M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 363 161.29
## 2 362 138.69 1 22.599 58.988 1.498e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 797.8815
## 2 2 744.6298##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -2.4082 0.2569 -9.373 < 2e-16 ***
## alpha 0.8997 0.1025 8.781 < 2e-16 ***
## A 12.8332 2.7013 4.751 2.93e-06 ***
## k 245.1081 64.9200 3.776 0.000187 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.619 on 362 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 2.09e-06
## (1 observation deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_M313, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_M313, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_M313, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 744.6298
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -2.4082 0.2569 -9.373 < 2e-16 ***
## alpha 0.8997 0.1025 8.781 < 2e-16 ***
## A 12.8332 2.7013 4.751 2.93e-06 ***
## k 245.1081 64.9200 3.776 0.000187 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.619 on 362 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 2.09e-06
## (1 observation deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.97345, p-value = 3.017e-06
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.1458, p-value = 0.2519
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1183 rows containing missing values (`geom_line()`).
plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1732 1220.2
## 2 1731 1096.0 1 124.22 196.2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4008.309
## 2 2 3824.022##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.68604 1.04125 0.659 0.51
## alpha 0.85623 0.05023 17.047 < 2e-16 ***
## A 2.15238 0.44063 4.885 1.13e-06 ***
## k 93.67208 13.89544 6.741 2.13e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7957 on 1731 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 3.555e-06
## (21 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_M331, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_M331, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_M331, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 3824.022
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.68604 1.04125 0.659 0.51
## alpha 0.85623 0.05023 17.047 < 2e-16 ***
## A 2.15238 0.44063 4.885 1.13e-06 ***
## k 93.67208 13.89544 6.741 2.13e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7957 on 1731 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 3.555e-06
## (21 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.86593, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.9821, p-value = 6.29e-07
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 10 rows containing missing values (`geom_point()`).## Warning: Removed 1091 rows containing missing values (`geom_line()`).
plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2513 1908.3
## 2 2512 1605.4 1 302.97 474.07 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 7070.262
## 2 2 6637.298##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.26573 0.28817 -4.392 1.17e-05 ***
## alpha 1.02015 0.03978 25.645 < 2e-16 ***
## A 7.81968 0.82996 9.422 < 2e-16 ***
## k 172.60172 17.04495 10.126 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7994 on 2512 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 5.624e-06
## (96 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_M332, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_M332, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_M332, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 6637.298
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.26573 0.28817 -4.392 1.17e-05 ***
## alpha 1.02015 0.03978 25.645 < 2e-16 ***
## A 7.81968 0.82996 9.422 < 2e-16 ***
## k 172.60172 17.04495 10.126 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7994 on 2512 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 5.624e-06
## (96 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.89792, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -5.4149, p-value = 6.132e-08
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 53 rows containing missing values (`geom_point()`).## Warning: Removed 1001 rows containing missing values (`geom_line()`).
plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1691 1694.2
## 2 1690 1407.8 1 286.39 343.79 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 5853.124
## 2 2 5541.443##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.73546 0.93704 0.785 0.433
## alpha 1.04434 0.04862 21.479 < 2e-16 ***
## A 5.89925 1.08977 5.413 7.07e-08 ***
## k 138.46540 14.07628 9.837 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9127 on 1690 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 9.943e-06
## (59 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_M333, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_M333, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_M333, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 5541.443
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.73546 0.93704 0.785 0.433
## alpha 1.04434 0.04862 21.479 < 2e-16 ***
## A 5.89925 1.08977 5.413 7.07e-08 ***
## k 138.46540 14.07628 9.837 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9127 on 1690 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 9.943e-06
## (59 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.91917, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.2412, p-value = 2.223e-05
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 25 rows containing missing values (`geom_point()`).## Warning: Removed 925 rows containing missing values (`geom_line()`).
plotting 2
M334 - Black Hills Coniferous Forest
Model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 355 278.17
## 2 354 253.55 1 24.62 34.374 1.043e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 925.8321
## 2 2 894.6559##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.1267 1.1013 -0.115 0.908493
## alpha 0.8505 0.1276 6.665 1.02e-10 ***
## A 2.3576 0.5852 4.029 6.87e-05 ***
## k 44.1368 12.9692 3.403 0.000742 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8463 on 354 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 2.009e-06
## (101 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
Model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", "_TI", sep = "")), data = G_M334, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "b", "_TI", sep = "")), data = G_M334, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## Error in nls(get(paste("fg_", Mod.Sel1, "c", "_TI", sep = "")), data = G_M334, :
## parameters without starting value in 'data': tau, phi, DeltaPDSI
## model AIC
## 1 2 894.6559
## 2 2a NA
## 3 2b NA
## 4 2c NA##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.1267 1.1013 -0.115 0.908493
## alpha 0.8505 0.1276 6.665 1.02e-10 ***
## A 2.3576 0.5852 4.029 6.87e-05 ***
## k 44.1368 12.9692 3.403 0.000742 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8463 on 354 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 2.009e-06
## (101 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.92289, p-value = 1.3e-12
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.998, p-value = 0.04572
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 46 rows containing missing values (`geom_point()`).## Warning: Removed 1264 rows containing missing values (`geom_line()`).
plotting 2
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
Model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
Model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 2 |
| 212 | Laurentian Mixed Forest | 2 |
| 221 | Eastern Broadleaf Forest | 2 |
| 222 | Midwest Broadleaf Forest | 2 |
| 223 | Central Interior Broadleaf Forest | 2 |
| 231 | Southeastern Mixed Forest | 2 |
| 232 | Outer Coastal Plain Mixed Forest | 2 |
| 234 | Lower Mississippi Riverine Forest | 2 |
| 242 | Pacific Lowland Mixed Forest | 2 |
| 251 | Prairie Parkland (Temperate) | 2 |
| 255 | Prairie Parkland (Subtropical) | NA |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | NA |
| 313 | Colorado Plateau Semi-Desert | 2 |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | NA |
| 332 | Great Plains Steppe | 2 |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | 2 |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 2 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 2 |
| M223 | Ozark Broadleaf Forest Meadow | 2 |
| M231 | Ouachita Mixed Forest | 2 |
| M242 | Cascade Mixed Forest | 2 |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 2 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | 2 |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | 2 |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | 2 |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | 2 |
| M334 | Black Hills Coniferous Forest | 2 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | NA |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | tau | tau.variance | tau.2.5 | tau.97.5 | alpha | alpha.variance | alpha.2.5 | alpha.97.5 | A | A.2.5 | A.97.5 | k | k.2.5 | k.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 6877 | 2876 | 0.5560549 | NA | 0.1957426 | 0.9163672 | 0.7961287 | NA | 0.7338538 | 0.8584037 | 4.925631 | 4.5213459 | 5.329917 | 113.43273 | 102.90326 | 123.96221 |
| 212 | Laurentian Mixed Forest | east | 22715 | 9499 | 1.1569991 | 0.0229510 | 0.8600536 | 1.4539446 | 1.0693906 | 0.0003781 | 1.0312759 | 1.1075053 | 5.573636 | 5.1836245 | 5.963647 | 213.12553 | 197.20902 | 229.04204 |
| 221 | Eastern Broadleaf Forest | east | 7333 | 3571 | -0.9302528 | 0.0122539 | -1.1472514 | -0.7132542 | 0.8226072 | 0.0013171 | 0.7514632 | 0.8937511 | 7.022411 | 6.4906234 | 7.554200 | 89.63813 | 78.48379 | 100.79246 |
| 222 | Midwest Broadleaf Forest | east | 5845 | 2589 | 0.2268005 | 0.0423833 | -0.1768023 | 0.6304033 | 0.9716370 | 0.0014746 | 0.8963549 | 1.0469190 | 6.841671 | 6.1293169 | 7.554025 | 141.35101 | 124.32769 | 158.37433 |
| 223 | Central Interior Broadleaf Forest | east | 10010 | 3864 | -0.9866708 | 0.0088774 | -1.1713637 | -0.8019779 | 0.8487235 | 0.0012289 | 0.7800073 | 0.9174397 | 11.868828 | 10.5383536 | 13.199302 | 263.34595 | 224.72766 | 301.96423 |
| 231 | Southeastern Mixed Forest | east | 13517 | 6193 | 1.5531887 | 0.0385956 | 1.1681035 | 1.9382739 | 0.9388297 | 0.0004387 | 0.8977761 | 0.9798833 | 5.087821 | 4.7357566 | 5.439886 | 62.69065 | 57.19405 | 68.18724 |
| 232 | Outer Coastal Plain Mixed Forest | east | 13629 | 6626 | 1.1066026 | 0.0361284 | 0.7340291 | 1.4791760 | 0.9624493 | 0.0004221 | 0.9221786 | 1.0027200 | 5.289786 | 4.9074756 | 5.672097 | 68.64871 | 62.76319 | 74.53423 |
| 234 | Lower Mississippi Riverine Forest | east | 1388 | 778 | 2.2159042 | 1.7228309 | -0.3590346 | 4.7908430 | 0.9884607 | 0.0083077 | 0.8096528 | 1.1672686 | 4.125290 | 2.5076923 | 5.742887 | 56.92517 | 36.49536 | 77.35498 |
| 242 | Pacific Lowland Mixed Forest | pacific | 83 | 83 | 1.6579938 | 13.9067684 | -5.7693078 | 9.0852954 | 0.7566029 | 0.0739823 | 0.2148743 | 1.2983314 | 6.246946 | -1.0823477 | 13.576240 | 90.62213 | 35.41388 | 145.83037 |
| 251 | Prairie Parkland (Temperate) | east | 2295 | 906 | 0.6811323 | 0.1817401 | -0.1549870 | 1.5172516 | 0.5486749 | 0.0081933 | 0.3711442 | 0.7262055 | 4.546017 | 3.6783426 | 5.413690 | 98.08080 | 74.73861 | 121.42298 |
| 255 | Prairie Parkland (Subtropical) | east | 717 | 319 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 261 | California Coastal Chaparral Forest and Shrub | pacific | 25 | 25 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | pacific | 163 | 161 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 313 | Colorado Plateau Semi-Desert | interior west | 218 | 218 | -0.7113609 | 1.5391215 | -3.1569456 | 1.7342238 | 0.9557811 | 0.0576489 | 0.4824754 | 1.4290868 | 4.548433 | 1.0582364 | 8.038630 | 189.58218 | 56.48993 | 322.67444 |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | interior west | 4 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | interior west | 9 | 9 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | interior west | 3 | 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | interior west | 331 | 255 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 332 | Great Plains Steppe | interior west | 232 | 128 | 1.7278114 | 5.2292244 | -2.7825658 | 6.2381886 | 0.7985898 | 0.0619002 | 0.3078623 | 1.2893172 | 4.203556 | 0.8602713 | 7.546841 | 144.41344 | 47.71849 | 241.10839 |
| 341 | Intermountain Semi-Desert and Desert | interior west | 66 | 64 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | interior west | 124 | 123 | 1.0856593 | 12.2095842 | -5.8383739 | 8.0096926 | 1.0512953 | 0.0419176 | 0.6455933 | 1.4569974 | 5.180998 | -1.5161196 | 11.878116 | 176.68258 | 35.43605 | 317.92912 |
| 411 | Everglades | east | 96 | 63 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 6772 | 3006 | 1.3789562 | 0.0716694 | 0.8541573 | 1.9037551 | 0.8139561 | 0.0008829 | 0.7557064 | 0.8722058 | 3.719861 | 3.3429453 | 4.096776 | 104.43455 | 93.90666 | 114.96245 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 8315 | 3810 | 0.3103333 | 0.0315639 | -0.0379292 | 0.6585959 | 0.8859613 | 0.0028000 | 0.7822337 | 0.9896889 | 5.649565 | 5.1102120 | 6.188918 | 105.48995 | 89.97177 | 121.00812 |
| M223 | Ozark Broadleaf Forest Meadow | east | 896 | 349 | 3.5517264 | 3.3238593 | -0.0264611 | 7.1299139 | 1.0186753 | 0.0240896 | 0.7140564 | 1.3232942 | 2.756220 | 1.2985300 | 4.213910 | 114.70220 | 50.69580 | 178.70860 |
| M231 | Ouachita Mixed Forest | east | 1006 | 495 | 5.0158823 | 6.6986410 | -0.0630660 | 10.0948307 | 0.9368876 | 0.0112811 | 0.7284593 | 1.1453160 | 2.720020 | 1.1336037 | 4.306437 | 114.48270 | 65.84720 | 163.11820 |
| M242 | Cascade Mixed Forest | pacific | 3224 | 3207 | -1.7349981 | 0.0474142 | -2.1619407 | -1.3080556 | 1.0116170 | 0.0042722 | 0.8834609 | 1.1397730 | 11.134460 | 9.4611196 | 12.807800 | 136.14898 | 116.87547 | 155.42249 |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | pacific | 1977 | 1807 | -1.1592275 | 0.1484890 | -1.9150294 | -0.4034256 | 0.9245881 | 0.0069847 | 0.7606675 | 1.0885086 | 12.070635 | 9.4204317 | 14.720838 | 258.31415 | 208.17166 | 308.45665 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | interior west | 30 | 26 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 367 | 367 | -2.4081804 | 0.0660097 | -2.9134306 | -1.9029302 | 0.8996779 | 0.0104966 | 0.6982007 | 1.1011551 | 12.833247 | 7.5211319 | 18.145361 | 245.10807 | 117.44044 | 372.77570 |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 1756 | 1756 | 0.6860443 | 1.0841993 | -1.3561940 | 2.7282827 | 0.8562318 | 0.0025230 | 0.7577159 | 0.9547477 | 2.152384 | 1.2881539 | 3.016615 | 93.67208 | 66.41846 | 120.92569 |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 2612 | 2602 | -1.2657294 | 0.0830425 | -1.8308064 | -0.7006525 | 1.0201463 | 0.0015824 | 0.9421415 | 1.0981511 | 7.819684 | 6.1921981 | 9.447169 | 172.60172 | 139.17812 | 206.02531 |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 1753 | 1742 | 0.7354562 | 0.8780488 | -1.1024298 | 2.5733421 | 1.0443357 | 0.0023640 | 0.9489728 | 1.1396987 | 5.899254 | 3.7618186 | 8.036690 | 138.46540 | 110.85662 | 166.07419 |
| M334 | Black Hills Coniferous Forest | interior west | 459 | 181 | -0.1266700 | 1.2128070 | -2.2925346 | 2.0391945 | 0.8505473 | 0.0162865 | 0.5995614 | 1.1015331 | 2.357583 | 1.2066361 | 3.508530 | 44.13678 | 18.63034 | 69.64322 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | interior west | 220 | 220 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot tau
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot alpha (biomass growth compensation effect)
plot A (asymptote of forest biomass growth in Mg/ha/yr)
## Warning: Removed 12 rows containing missing values (`geom_point()`).
plot k (stand biomass at half biomss G in Mg/ha)
## Warning: Removed 12 rows containing missing values (`geom_point()`).
Caclulations - weighted averages
tau (productivity trend (in %) 2000-2021)
## region weighted.tau weighted.tau.std_Error 95 % CI, upper
## 1 entire US 0.43761120 0.12962494 0.69167607
## 2 pacific -0.12984462 0.01866564 -0.09325997
## 3 east 0.59360480 0.11934977 0.82753035
## 4 interior west -0.02614898 0.04700905 0.06598875
## 95 % CI, lower
## 1 0.1835463
## 2 -0.1664293
## 3 0.3596793
## 4 -0.1182867alpha (effect of DeltaPDSI)
## region weighted.phi weighted.phi.std_Error 95 % CI, upper
## 1 entire US 0 0 0
## 2 pacific 0 0 0
## 3 east 0 0 0
## 4 interior west 0 0 0
## 95 % CI, lower
## 1 0
## 2 0
## 3 0
## 4 0A (asymptote of forest biomass growth in Mg/ha/yr)
## region weighted.A
## 1 entire US 6.291036
## 2 pacific 10.985869
## 3 east 5.888741
## 4 interior west 5.417810K (stand biomass at half biomss G in Mg/ha)
## region weighted.k
## 1 entire US 134.4559
## 2 pacific 172.4257
## 3 east 130.0094
## 4 interior west 134.3588